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      "Building wheels for collected packages: pathtools\n",
      "  Building wheel for pathtools (setup.py) ... \u001B[?25l\u001B[?25hdone\n",
      "  Created wheel for pathtools: filename=pathtools-0.1.2-py3-none-any.whl size=8791 sha256=67dd2649631e136cd3d36754e42db228bfcf2024ea6e8f1e44d6158a20901bd3\n",
      "  Stored in directory: /root/.cache/pip/wheels/e7/f3/22/152153d6eb222ee7a56ff8617d80ee5207207a8c00a7aab794\n",
      "Successfully built pathtools\n",
      "Installing collected packages: tokenizers, sentencepiece, safetensors, pathtools, bitsandbytes, xxhash, smmap, setproctitle, sentry-sdk, mypy-extensions, marshmallow, docker-pycreds, dill, typing-inspect, multiprocess, huggingface-hub, gitdb, transformers, langsmith, GitPython, dataclasses-json, wandb, langchain, datasets, accelerate, trl, peft\n",
      "Successfully installed GitPython-3.1.32 accelerate-0.22.0 bitsandbytes-0.41.1 dataclasses-json-0.5.14 datasets-2.14.4 dill-0.3.7 docker-pycreds-0.4.0 gitdb-4.0.10 huggingface-hub-0.16.4 langchain-0.0.273 langsmith-0.0.26 marshmallow-3.20.1 multiprocess-0.70.15 mypy-extensions-1.0.0 pathtools-0.1.2 peft-0.5.0 safetensors-0.3.3 sentencepiece-0.1.99 sentry-sdk-1.29.2 setproctitle-1.3.2 smmap-5.0.0 tokenizers-0.13.3 transformers-4.32.0 trl-0.6.0 typing-inspect-0.9.0 wandb-0.15.8 xxhash-3.3.0\n"
     ]
    }
   ],
   "source": [
    "!pip install -U accelerate bitsandbytes datasets transformers peft trl sentencepiece wandb langchain"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "!pip show accelerate bitsandbytes datasets transformers peft trl sentencepiece wandb langchain"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "T_TZjUpIVxp0",
    "outputId": "aabdcb2d-c72d-4e7a-8171-3f32ac1aa901"
   },
   "execution_count": 14,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Name: accelerate\n",
      "Version: 0.22.0\n",
      "Summary: Accelerate\n",
      "Home-page: https://github.com/huggingface/accelerate\n",
      "Author: The HuggingFace team\n",
      "Author-email: sylvain@huggingface.co\n",
      "License: Apache\n",
      "Location: /usr/local/lib/python3.10/dist-packages\n",
      "Requires: numpy, packaging, psutil, pyyaml, torch\n",
      "Required-by: peft, trl\n",
      "---\n",
      "Name: bitsandbytes\n",
      "Version: 0.41.1\n",
      "Summary: k-bit optimizers and matrix multiplication routines.\n",
      "Home-page: https://github.com/TimDettmers/bitsandbytes\n",
      "Author: Tim Dettmers\n",
      "Author-email: dettmers@cs.washington.edu\n",
      "License: MIT\n",
      "Location: /usr/local/lib/python3.10/dist-packages\n",
      "Requires: \n",
      "Required-by: \n",
      "---\n",
      "Name: datasets\n",
      "Version: 2.14.4\n",
      "Summary: HuggingFace community-driven open-source library of datasets\n",
      "Home-page: https://github.com/huggingface/datasets\n",
      "Author: HuggingFace Inc.\n",
      "Author-email: thomas@huggingface.co\n",
      "License: Apache 2.0\n",
      "Location: /usr/local/lib/python3.10/dist-packages\n",
      "Requires: aiohttp, dill, fsspec, huggingface-hub, multiprocess, numpy, packaging, pandas, pyarrow, pyyaml, requests, tqdm, xxhash\n",
      "Required-by: trl\n",
      "---\n",
      "Name: transformers\n",
      "Version: 4.32.0\n",
      "Summary: State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow\n",
      "Home-page: https://github.com/huggingface/transformers\n",
      "Author: The Hugging Face team (past and future) with the help of all our contributors (https://github.com/huggingface/transformers/graphs/contributors)\n",
      "Author-email: transformers@huggingface.co\n",
      "License: Apache 2.0 License\n",
      "Location: /usr/local/lib/python3.10/dist-packages\n",
      "Requires: filelock, huggingface-hub, numpy, packaging, pyyaml, regex, requests, safetensors, tokenizers, tqdm\n",
      "Required-by: peft, trl\n",
      "---\n",
      "Name: peft\n",
      "Version: 0.5.0\n",
      "Summary: Parameter-Efficient Fine-Tuning (PEFT)\n",
      "Home-page: https://github.com/huggingface/peft\n",
      "Author: The HuggingFace team\n",
      "Author-email: sourab@huggingface.co\n",
      "License: Apache\n",
      "Location: /usr/local/lib/python3.10/dist-packages\n",
      "Requires: accelerate, numpy, packaging, psutil, pyyaml, safetensors, torch, tqdm, transformers\n",
      "Required-by: \n",
      "---\n",
      "Name: trl\n",
      "Version: 0.6.0\n",
      "Summary: A Pytorch implementation of Proximal Policy Optimization for transfomer language models.\n",
      "Home-page: https://github.com/huggingface/trl\n",
      "Author: Leandro von Werra\n",
      "Author-email: leandro.vonwerra@gmail.com\n",
      "License: Apache 2.0\n",
      "Location: /usr/local/lib/python3.10/dist-packages\n",
      "Requires: accelerate, datasets, numpy, torch, transformers\n",
      "Required-by: \n",
      "---\n",
      "Name: sentencepiece\n",
      "Version: 0.1.99\n",
      "Summary: SentencePiece python wrapper\n",
      "Home-page: https://github.com/google/sentencepiece\n",
      "Author: Taku Kudo\n",
      "Author-email: taku@google.com\n",
      "License: Apache\n",
      "Location: /usr/local/lib/python3.10/dist-packages\n",
      "Requires: \n",
      "Required-by: \n",
      "---\n",
      "Name: wandb\n",
      "Version: 0.15.8\n",
      "Summary: A CLI and library for interacting with the Weights and Biases API.\n",
      "Home-page: https://github.com/wandb/wandb\n",
      "Author: Weights & Biases\n",
      "Author-email: support@wandb.com\n",
      "License: MIT license\n",
      "Location: /usr/local/lib/python3.10/dist-packages\n",
      "Requires: appdirs, Click, docker-pycreds, GitPython, pathtools, protobuf, psutil, PyYAML, requests, sentry-sdk, setproctitle, setuptools\n",
      "Required-by: \n",
      "---\n",
      "Name: langchain\n",
      "Version: 0.0.273\n",
      "Summary: Building applications with LLMs through composability\n",
      "Home-page: https://github.com/langchain-ai/langchain\n",
      "Author: \n",
      "Author-email: \n",
      "License: MIT\n",
      "Location: /usr/local/lib/python3.10/dist-packages\n",
      "Requires: aiohttp, async-timeout, dataclasses-json, langsmith, numexpr, numpy, pydantic, PyYAML, requests, SQLAlchemy, tenacity\n",
      "Required-by: \n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "from huggingface_hub import notebook_login\n",
    "notebook_login()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 145,
     "referenced_widgets": [
      "1e34a2a16c544c51ac5c0853d629728b",
      "1ac6f9e7693b415ba809ec99ca586cbd",
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      "868d50d178c748de9fd07e20af200731",
      "87820514ed734025ae44232c8d15a8cf",
      "d9a0af84274c45f2b042b881061bf0ff",
      "7defbd7e167c4fb99cac7694b61be35d",
      "aa04541d8c7f4befa0ec41e197fb17d6",
      "e9ab8c262e794e328aa2d2abd43edbf2",
      "cb76bf2ea2824543bd718492ae4a420d",
      "e63cd9618ba0479eb656d5f71089bbd9",
      "05e799b1723a461bb2643cd27818168a",
      "ba372b5f87404560af66f56b89cfdfb2",
      "308fdcce03f8491eb3e0a4a29efd34d1",
      "6b18d406780a4eb2843e01f60025d958",
      "dce32942e49943079657218ea20040bf",
      "b54fbb25c8ee4fafb50bda29225c7c27",
      "20f9753defbc4129b77e7ff4a2ae112a",
      "8333faa7620b46e38f98e0103f6f182e",
      "bd567a70a0924d95b3cd1dbe73dfab62",
      "66efc2d7d534475894de23dd01bec0f3",
      "ac19c89b49754da6af6bde3908703f8e",
      "507817b6d6e646b99eed29a989c73011",
      "85d05b38e28547efac034c4435cd231f",
      "311ab8e175c8458dbcc6c3b12d3a1aa2",
      "28d313ffbcd34562bc76cdbe39c0dbaa",
      "7d4bac6f3bd5461f89966b6bcbc77574",
      "230194df5fc2421dbca06a122007587c",
      "870ceb127f474b58a64cf1a1d8b03364",
      "ffc00b5c3eb74da49e2f6830d838c57f",
      "2e3bef3a5da44904943ce1a6ca7b09b9",
      "f173d56d0f9c459abda33003ef360162"
     ]
    },
    "id": "f1k_uTqTF1jy",
    "outputId": "a50abbf9-e624-411b-fed8-463064ddb410"
   },
   "execution_count": 2,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "1e34a2a16c544c51ac5c0853d629728b"
      }
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "import os\n",
    "os.environ[\"WANDB_PROJECT\"] = \"finetuning\""
   ],
   "metadata": {
    "id": "kWgT2HHGZPfA"
   },
   "execution_count": 3,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "import wandb\n",
    "wandb.login()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 124
    },
    "id": "mhPVjAl2EijY",
    "outputId": "42d265bf-378d-4b20-d957-3c77ae24354d"
   },
   "execution_count": 4,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ],
      "application/javascript": [
       "\n",
       "        window._wandbApiKey = new Promise((resolve, reject) => {\n",
       "            function loadScript(url) {\n",
       "            return new Promise(function(resolve, reject) {\n",
       "                let newScript = document.createElement(\"script\");\n",
       "                newScript.onerror = reject;\n",
       "                newScript.onload = resolve;\n",
       "                document.body.appendChild(newScript);\n",
       "                newScript.src = url;\n",
       "            });\n",
       "            }\n",
       "            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n",
       "            const iframe = document.createElement('iframe')\n",
       "            iframe.style.cssText = \"width:0;height:0;border:none\"\n",
       "            document.body.appendChild(iframe)\n",
       "            const handshake = new Postmate({\n",
       "                container: iframe,\n",
       "                url: 'https://wandb.ai/authorize'\n",
       "            });\n",
       "            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n",
       "            handshake.then(function(child) {\n",
       "                child.on('authorize', data => {\n",
       "                    clearTimeout(timeout)\n",
       "                    resolve(data)\n",
       "                });\n",
       "            });\n",
       "            })\n",
       "        });\n",
       "    "
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "\u001B[34m\u001B[1mwandb\u001B[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
      "\u001B[34m\u001B[1mwandb\u001B[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
      "wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ··········\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "\u001B[34m\u001B[1mwandb\u001B[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "metadata": {},
     "execution_count": 4
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "if wandb.run is not None:\n",
    "  wandb.finish()"
   ],
   "metadata": {
    "id": "HxC2eHlLT_KA"
   },
   "execution_count": 17,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "dataset_name = \"squad_v2\"\n",
    "dataset = load_dataset(dataset_name, split=\"train\")\n",
    "eval_dataset = load_dataset(dataset_name, split=\"validation\")"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 305,
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    },
    "id": "MU9_bGIFQUeT",
    "outputId": "0ffc4929-91b4-44b1-e7f7-7d6a56c94d39"
   },
   "execution_count": 6,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading builder script:   0%|          | 0.00/5.28k [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "ed89e0816ca4462197ed55e275488e8b"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading metadata:   0%|          | 0.00/2.40k [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "b2a4fd0e63d2477cae43a049b04f80b4"
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     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading readme:   0%|          | 0.00/8.02k [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "c01e2f7a902c40b388f9fd49ec95e9b3"
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     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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       "model_id": "d7c21ee148ef410a8cf68649fcad8fb1"
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     "metadata": {}
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    {
     "output_type": "display_data",
     "data": {
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      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "d0a5cd1380714c76bd1af4386ab237ba"
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     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Generating train split:   0%|          | 0/130319 [00:00<?, ? examples/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "ee825de7dac64c26bfdd35f28b8cfb45"
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     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Generating validation split:   0%|          | 0/11873 [00:00<?, ? examples/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
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      }
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "load_dataset(dataset_name)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AD1oFjKTSuSM",
    "outputId": "be29adf0-031e-4910-806a-15f03f6a874b"
   },
   "execution_count": 7,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['id', 'title', 'context', 'question', 'answers'],\n",
       "        num_rows: 130319\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['id', 'title', 'context', 'question', 'answers'],\n",
       "        num_rows: 11873\n",
       "    })\n",
       "})"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "dataset.features"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ezx6XVbCQcp_",
    "outputId": "3a6137f8-fbe1-4aa9-868f-2afa4ae4fe06"
   },
   "execution_count": 8,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'id': Value(dtype='string', id=None),\n",
       " 'title': Value(dtype='string', id=None),\n",
       " 'context': Value(dtype='string', id=None),\n",
       " 'question': Value(dtype='string', id=None),\n",
       " 'answers': Sequence(feature={'text': Value(dtype='string', id=None), 'answer_start': Value(dtype='int32', id=None)}, length=-1, id=None)}"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "model_id = \"openlm-research/open_llama_3b_v2\""
   ],
   "metadata": {
    "id": "vz5D8ndPOha9"
   },
   "execution_count": 9,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "import torch\n",
    "from transformers import AutoModelForCausalLM, BitsAndBytesConfig\n",
    "\n",
    "bnb_config = BitsAndBytesConfig(\n",
    "    load_in_4bit=True,\n",
    "    bnb_4bit_quant_type=\"nf4\",\n",
    "    bnb_4bit_compute_dtype=torch.float16,\n",
    ")\n",
    "device_map=\"auto\"\n",
    "\n",
    "base_model = AutoModelForCausalLM.from_pretrained(\n",
    "    model_id,\n",
    "    quantization_config=bnb_config,\n",
    "    device_map=\"auto\",\n",
    "    trust_remote_code=True,\n",
    ")\n",
    "base_model.config.use_cache = False"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 399
    },
    "id": "3uSvTxP0QhAO",
    "outputId": "79e74908-e049-4cd9-ef41-d984a49ffeb8"
   },
   "execution_count": 18,
   "outputs": [
    {
     "output_type": "error",
     "ename": "KeyboardInterrupt",
     "evalue": "ignored",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mKeyboardInterrupt\u001B[0m                         Traceback (most recent call last)",
      "\u001B[0;32m<ipython-input-18-c3929e2dde2e>\u001B[0m in \u001B[0;36m<cell line: 12>\u001B[0;34m()\u001B[0m\n\u001B[1;32m     10\u001B[0m \u001B[0mdevice_map\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;34m\"auto\"\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     11\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 12\u001B[0;31m base_model = AutoModelForCausalLM.from_pretrained(\n\u001B[0m\u001B[1;32m     13\u001B[0m     \u001B[0mmodel_id\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     14\u001B[0m     \u001B[0mquantization_config\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mbnb_config\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/auto/auto_factory.py\u001B[0m in \u001B[0;36mfrom_pretrained\u001B[0;34m(cls, pretrained_model_name_or_path, *model_args, **kwargs)\u001B[0m\n\u001B[1;32m    514\u001B[0m         \u001B[0;32melif\u001B[0m \u001B[0mtype\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mconfig\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;32min\u001B[0m \u001B[0mcls\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_model_mapping\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mkeys\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    515\u001B[0m             \u001B[0mmodel_class\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0m_get_model_class\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mconfig\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mcls\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_model_mapping\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 516\u001B[0;31m             return model_class.from_pretrained(\n\u001B[0m\u001B[1;32m    517\u001B[0m                 \u001B[0mpretrained_model_name_or_path\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m*\u001B[0m\u001B[0mmodel_args\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mconfig\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mconfig\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mhub_kwargs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    518\u001B[0m             )\n",
      "\u001B[0;32m/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py\u001B[0m in \u001B[0;36mfrom_pretrained\u001B[0;34m(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, *model_args, **kwargs)\u001B[0m\n\u001B[1;32m   2803\u001B[0m             \u001B[0;32mif\u001B[0m \u001B[0;32mnot\u001B[0m \u001B[0mis_sharded\u001B[0m \u001B[0;32mand\u001B[0m \u001B[0mstate_dict\u001B[0m \u001B[0;32mis\u001B[0m \u001B[0;32mNone\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   2804\u001B[0m                 \u001B[0;31m# Time to load the checkpoint\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m-> 2805\u001B[0;31m                 \u001B[0mstate_dict\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mload_state_dict\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mresolved_archive_file\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m   2806\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   2807\u001B[0m             \u001B[0;31m# set dtype to instantiate the model under:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py\u001B[0m in \u001B[0;36mload_state_dict\u001B[0;34m(checkpoint_file)\u001B[0m\n\u001B[1;32m    477\u001B[0m         \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    478\u001B[0m             \u001B[0mmap_location\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;34m\"cpu\"\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 479\u001B[0;31m         \u001B[0;32mreturn\u001B[0m \u001B[0mtorch\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mload\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mcheckpoint_file\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mmap_location\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mmap_location\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    480\u001B[0m     \u001B[0;32mexcept\u001B[0m \u001B[0mException\u001B[0m \u001B[0;32mas\u001B[0m \u001B[0me\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    481\u001B[0m         \u001B[0;32mtry\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\u001B[0m in \u001B[0;36mload\u001B[0;34m(f, map_location, pickle_module, weights_only, **pickle_load_args)\u001B[0m\n\u001B[1;32m    807\u001B[0m                     \u001B[0;32mexcept\u001B[0m \u001B[0mRuntimeError\u001B[0m \u001B[0;32mas\u001B[0m \u001B[0me\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    808\u001B[0m                         \u001B[0;32mraise\u001B[0m \u001B[0mpickle\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mUnpicklingError\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mUNSAFE_MESSAGE\u001B[0m \u001B[0;34m+\u001B[0m \u001B[0mstr\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0me\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;32mfrom\u001B[0m \u001B[0;32mNone\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 809\u001B[0;31m                 \u001B[0;32mreturn\u001B[0m \u001B[0m_load\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mopened_zipfile\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mmap_location\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mpickle_module\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mpickle_load_args\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    810\u001B[0m         \u001B[0;32mif\u001B[0m \u001B[0mweights_only\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    811\u001B[0m             \u001B[0;32mtry\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\u001B[0m in \u001B[0;36m_load\u001B[0;34m(zip_file, map_location, pickle_module, pickle_file, **pickle_load_args)\u001B[0m\n\u001B[1;32m   1170\u001B[0m     \u001B[0munpickler\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mUnpicklerWrapper\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdata_file\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mpickle_load_args\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1171\u001B[0m     \u001B[0munpickler\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mpersistent_load\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mpersistent_load\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m-> 1172\u001B[0;31m     \u001B[0mresult\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0munpickler\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mload\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m   1173\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1174\u001B[0m     \u001B[0mtorch\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_utils\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_validate_loaded_sparse_tensors\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\u001B[0m in \u001B[0;36mpersistent_load\u001B[0;34m(saved_id)\u001B[0m\n\u001B[1;32m   1140\u001B[0m         \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1141\u001B[0m             \u001B[0mnbytes\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mnumel\u001B[0m \u001B[0;34m*\u001B[0m \u001B[0mtorch\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_utils\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_element_size\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdtype\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m-> 1142\u001B[0;31m             \u001B[0mtyped_storage\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mload_tensor\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdtype\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mnbytes\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mkey\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0m_maybe_decode_ascii\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mlocation\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m   1143\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1144\u001B[0m         \u001B[0;32mreturn\u001B[0m \u001B[0mtyped_storage\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\u001B[0m in \u001B[0;36mload_tensor\u001B[0;34m(dtype, numel, key, location)\u001B[0m\n\u001B[1;32m   1110\u001B[0m         \u001B[0mname\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;34mf'data/{key}'\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1111\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m-> 1112\u001B[0;31m         \u001B[0mstorage\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mzip_file\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mget_storage_from_record\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mname\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mnumel\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mtorch\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mUntypedStorage\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_typed_storage\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_untyped_storage\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m   1113\u001B[0m         \u001B[0;31m# TODO: Once we decide to break serialization FC, we can\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1114\u001B[0m         \u001B[0;31m# stop wrapping with TypedStorage\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mKeyboardInterrupt\u001B[0m: "
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/gdrive')"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "t8qxSsZcTz_H",
    "outputId": "1265d4ac-6dd7-4132-d4c1-c8ef2fe93ec7"
   },
   "execution_count": 11,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Mounted at /content/gdrive\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "from transformers import AutoTokenizer, TrainingArguments, EarlyStoppingCallback\n",
    "from peft import LoraConfig\n",
    "from trl import SFTTrainer\n",
    "\n",
    "# More info: https://github.com/huggingface/transformers/pull/24906\n",
    "base_model.config.pretraining_tp = 1\n",
    "\n",
    "peft_config = LoraConfig(\n",
    "    lora_alpha=16,\n",
    "    lora_dropout=0.1,\n",
    "    r=64,\n",
    "    bias=\"none\",\n",
    "    task_type=\"CAUSAL_LM\",\n",
    ")\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)\n",
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "tokenizer.padding_side = \"right\"\n",
    "\n",
    "# if you don't want to use google drive, just set this to a directory on your computer:\n",
    "output_dir = \"/content/gdrive/My Drive/results\"\n",
    "new_model_name = f\"openllama-3b-peft-{dataset_name}\"\n",
    "\n",
    "training_args = TrainingArguments(\n",
    "    output_dir=output_dir,  # use new_model_name if saving to hf\n",
    "    per_device_train_batch_size=4,\n",
    "    gradient_accumulation_steps=4,\n",
    "    learning_rate=2e-4,\n",
    "    logging_steps=10,\n",
    "    max_steps=2000,  # training can still improve after many steps!\n",
    "    num_train_epochs=100,\n",
    "    evaluation_strategy=\"steps\",\n",
    "    eval_steps=5,  # update steps between two evaluations\n",
    "    save_total_limit=5,  # only last 5 models are saved\n",
    "    push_to_hub=False,  # you can set this to true if you want to upload your model to huggingspace\n",
    "    load_best_model_at_end=True,  # to use in combination with early stopping\n",
    "    report_to=\"wandb\"\n",
    ")\n",
    "\n",
    "max_seq_length = 512\n",
    "\n",
    "trainer = SFTTrainer(\n",
    "    model=base_model,\n",
    "    train_dataset=dataset,\n",
    "    eval_dataset=eval_dataset,\n",
    "    peft_config=peft_config,\n",
    "    dataset_text_field=\"question\",  # this depends on the dataset!\n",
    "    max_seq_length=max_seq_length,\n",
    "    tokenizer=tokenizer,\n",
    "    args=training_args,\n",
    "    callbacks=[EarlyStoppingCallback(early_stopping_patience=200)]\n",
    ")\n",
    "\n",
    "trainer.train()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 253,
     "referenced_widgets": [
      "11775873a0734a0591a84aa238e34c40",
      "eeb0bd7ad3664560beb9d88ac7ad806b",
      "af38836193eb47b98582bd294112175b",
      "6a6662babc38480eaa160649ed65891b",
      "59eb17756fbb4a2fa6c5a0bef81def63",
      "13666b2fc2934041a643b2fe89d1fb60",
      "c9e6d78db56b4f04a4225d337b1a5b1e",
      "02485fedd8334b4184dd23d7a25d102d",
      "44ca70e905eb49a7ae86df2f4216b742",
      "36f8d4ae4b284137aa17f2255090447c",
      "763e3accf2844c0db240aea37eb476e2"
     ]
    },
    "id": "WWIvXYm2QuMw",
    "outputId": "64e679b0-5dc0-402e-e870-1b290e4623c3"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Map:   0%|          | 0/11873 [00:00<?, ? examples/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "11775873a0734a0591a84aa238e34c40"
      }
     },
     "metadata": {}
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    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ],
      "text/html": [
       "Tracking run with wandb version 0.15.8"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ],
      "text/html": [
       "Run data is saved locally in <code>/content/wandb/run-20230825_123411-1aev1nma</code>"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ],
      "text/html": [
       "Syncing run <strong><a href='https://wandb.ai/auffarth/finetuning/runs/1aev1nma' target=\"_blank\">lilac-gorge-4</a></strong> to <a href='https://wandb.ai/auffarth/finetuning' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ],
      "text/html": [
       " View project at <a href='https://wandb.ai/auffarth/finetuning' target=\"_blank\">https://wandb.ai/auffarth/finetuning</a>"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ],
      "text/html": [
       " View run at <a href='https://wandb.ai/auffarth/finetuning/runs/1aev1nma' target=\"_blank\">https://wandb.ai/auffarth/finetuning/runs/1aev1nma</a>"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ],
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='6' max='2000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [   6/2000 00:17 < 2:21:50, 0.23 it/s, Epoch 0.00/1]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table><p>\n",
       "    <div>\n",
       "      \n",
       "      <progress value='297' max='1485' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [ 297/1485 03:10 < 12:45, 1.55 it/s]\n",
       "    </div>\n",
       "    "
      ]
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "trainer.model.save_pretrained(\n",
    "    os.path.join(output_dir, \"final_checkpoint\"),\n",
    ")"
   ],
   "metadata": {
    "id": "e5gJjiIrm97E"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "trainer.model.push_to_hub(\n",
    "    repo_id=new_model_name\n",
    ")"
   ],
   "metadata": {
    "id": "hhb8AOiQeSqU"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "# usually, the peft model is stored as an adapter, not as a full model, therefore the loading is a bit different:\n",
    "from peft import PeftModel, PeftConfig\n",
    "from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline\n",
    "from langchain.llms import HuggingFacePipeline\n",
    "\n",
    "model_id = 'openlm-research/open_llama_3b_v2'\n",
    "config = PeftConfig.from_pretrained(\"benji1a/openllama-3b-peft-squad_v2\")\n",
    "model = AutoModelForCausalLM.from_pretrained(model_id)\n",
    "model = PeftModel.from_pretrained(model, \"benji1a/openllama-3b-peft-squad_v2\")\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)\n",
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "pipe = pipeline(\n",
    "    \"text-generation\",\n",
    "    model=model,\n",
    "    tokenizer=tokenizer,\n",
    "    max_length=256\n",
    ")\n",
    "llm = HuggingFacePipeline(pipeline=pipe)"
   ],
   "metadata": {
    "id": "imvnXAlxbGj8"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [],
   "metadata": {
    "id": "8PegBOhjprv0"
   },
   "execution_count": null,
   "outputs": []
  }
 ]
}
